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• W204760306 abstract "Community structure is an important aspect of network analysis, with a variety of reallifeapplications. Local community detection algorithms, which are relatively new inliterature, provide the opportunity to analyze community structure in large networkswithout needing global information. We focus our work on a state-of-the-art algorithmdeveloped by Yang and Leskovec and evaluate it on three dierent networks: Amazon,DBLP and Soundcloud.We highlight various similarities and dierences between the geometry and the sizesof real and annotated communities. The algorithm shows robustness to the seed node,which is also demonstrated by its rather high level of stability. By using two dierentmethods of seed selection from the literature, we demonstrate further improvement onthe quality of the communities returned by the algorithm. Finally, we try to detect reallifecommunities and show that the local algorithm is comparable to global algorithmsin terms of accuracy." @default.
• W204760306 created "2016-06-24" @default.
• W204760306 creator A5091026275 @default.
• W204760306 date "2014-01-01" @default.
• W204760306 modified "2023-09-22" @default.
• W204760306 title "Local Community Detection in Complex Networks" @default.
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